Study on grey prediction model GM(1,1) and its application based on amplitude compression transformation

被引：0
|
作者：
Qian Wu-yong [1 ]
Dang Yao-guo [2 ]
Liu Si-feng [2 ]
机构：
[1] Jiangnan Univ, Sch Business, Wuxi, Peoples R China
[2] Nanjing Univ Aeronaut & Astronaut, Coll Econ & Management, Nanjing, Peoples R China
来源：
PROCEEDINGS OF 2013 IEEE INTERNATIONAL CONFERENCE ON GREY SYSTEMS AND INTELLIGENT SERVICES (GSIS) | 2013年
基金：
中国国家自然科学基金;
关键词：
Grey System; Fluctuation Sequences; Amplitude compression transformation; Model GM(1,1);
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
With respect to the issue that traditional grey model GM(1,1) hardly satisfies the prediction requirement of sequence fluctuation, a new data processing method of dealing with amplitude compression transformation and average weighted transformation is proposed. Furthermore, the fact that amplitude compression transformation is able to decrease the fluctuation of sequences and average weighted transformation can enhance the smoothness of sequences proved theoretically. Grey prediction model GM(1,1) is established based on applying amplitude compression transformation and average weighted transformation to exchange the fluctuation sequences. Finally, an application to forecast China's extraordinarily serious road traffic accidents is presented to illustrate the proposed framework
引用
收藏
页码：103 / 107
页数：5
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